Will Larson's Web Page

Current version of my urban decline paper:

Housing and Labor Market Dynamics in Growing Versus Declining Cities

William D. Larson
George Washington University
Monroe Hall #340
2115 G St NW
Washington, DC 20052
Tel: (202) 557-9930
Fax: (202) 994-6147
Email: larsonwd@gmail.com

Abstract:

This paper reconciles a debate on the nature of regional supply responses to demand shocks. Cities are found to exhibit dramatically different housing and labor market dynamics in response to local demand shocks, consistent with the hypothesis that the durable nature of the housing stock acts as a supply constraint in declining cities. These results imply that demand-driven models are appropriate in growing or stable cities, and models with supply constraints are more appropriate in declining cities. Failure to apply the correct class of models to a particular city will result in biased estimated employment, house price, and wage effects of both market-based demand shocks and demand-side stimulus policies.

JEL Classifications: R23, R31, R58.

Keywords: housing supply, urban decline, export prices, development policies

I thank Anthony Yezer, Fred Joutz, Donald Parsons, and Tara Sinclair for their untiring help and support. I also thank Timothy Bartik, Joshua Gallin, Mike Hollar, Lutz Kilian, Steve Malpezzi,  Raven Saks Molloy, Mike Owyang, and Albert Saiz for their helpful data, programs, conversations, and correspondence. I would also like to thank seminar participants at the George Washington University and the AREUEA session at 2011 ASSA meetings.

Update!

There have been several things of note recently:

1) I finished grad school at GWU this spring and am now a newly minted PhD in economics! I have some hard copies of my dissertation and they look really good on my bookshelf!

2) Three weeks ago, I started as Manager of Audience Forecasting and Modeling in the Research and Analytics department at the Washington Post. I’ve been working on exciting projects so far, and it’s a great place to be.

3) I’ve presented my house price forecasting paper (http://www.gwu.edu/~forcpgm/2010-004.pdf) at the AREUEA mid-year meetings on June 3 and at HUD at June 28th.

Latex seminar

This note is an addendum to the seminar on Latex I’m giving at GWU on April 13, 2011.

• Document template (pdf, tex, gif, eps, xls)
• Bibtex template (tex)
• Beamer presentation (pdf, tex, gif, eps)
• Dissertation template (fits with GW formatting guidelines!)
• post-script to pdf
• images to eps
• excel to tables
• Compiler and package manager: Miktex
• Text Editor (with Latex compiler shortcuts): LEd

Baseball player forecast evaluations

I’ve been moonlighting a bit recently doing some baseball player forecast evaluations. Specifically, I’ve been looking at the quality of different sets of projections made by some of the more influential people and organizations online (CBS, ESPN, and three others), and a crowd-sourced set of projections made by the community at fangraphs.com.  I look at their ability to forecast five hitting categories: batting average, runs, home runs, rbis, and stolen bases.  I find that one of the mechanical projections, the “Marcel projections,” and the crowd-sourced fan projections have the most unique and useful information in them.

For more, check out my featured contributions at fangraphs: Part 1. Part 2.

House price forecasting paper available on SSRN

Job market paper available on SSRN

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1702488

Housing and Labor Market Dynamics in Growing Versus Declining Cities

This paper develops a regional equilibrium model motivated by Glaeser and Gyourko’s (2005) urban decline hypothesis. The durability of the housing stock creates differences in the elasticity of housing supply in growing versus declining cities. These differences cause local demand shocks to have larger effects on employment and smaller effects on wages and house prices in growing or stable cities relative to declining cities. Empirical results suggest labor market hysteresis is conditional on a city’s recent history of growth or decline. In growing cities, temporary local demand shocks cause housing construction which leads to permanently higher levels of employment but no changes to local prices. In declining cities, local demand shocks induce very little construction and thus exhibit small long-run employment effects, but permanent price effects remain.

Testing hypotheses about the effects of demand shocks on regional economies has been limited by the inability to identify demand shocks at the regional level. This problem is solved by relying on an export price index (EPI), an index of prices local producers receive when exporting goods and services outside of the region. The empirical approach involves estimating a near-VAR for each of 352 U.S. cities with eight variables separated into three block-exogenous groups: national variables, the EPI, and local variables. Characteristics of the city-level impulse responses are then estimated as a function of an index of urban decline, which is found to have a significant effect on the nature of labor and housing market responses.

Energy footprint paper available on SSRN

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1696056

Energy Footprint of the City: Effects of Urban Land Use and Transportation Policies

Urban land use and transportation policies have dramatic effects on the spatial distribution of residences and commuting patterns of large cities. Some of these policies have been analyzed using numerical urban simulation models. At the same time, the U.S. Energy Information Administration’s Residential Energy Consumption Survey has allowed researchers to investigate the relation between household energy consumption and characteristics of housing units.

This paper links these two lines of inquiry so that simulation results on the implications of land use and transportation policies for the spatial form of cities can be used to compute implications for energy consumption. The resulting Urban Energy Footprint Model, “UEFM”, allows one to trace the implications of a change in land use zoning or transportation through its effects on housing markets and residential location to the consequent changes in energy use for residential and commuting purposes – i.e. to understand the energy footprint of transportation, housing, and land use policies. Accordingly, the UEFM provides, perhaps for the first time, a link between urban and energy economics.

Doctoral Dissertation Research Grant

I was just awarded a Doctoral Dissertation Research Grant from HUD. This grant will provide funding for me to continue my research on house price forecasts and forecast evaluations. Thank you HUD for supporting my research! Below is the summary:

The recent, dramatic declines in house prices have drawn attention to our ability to forecast house prices. In this essay, I directly address two questions: 1) could econometric forecasts have predicted the recent downturn in house prices before any declines in house prices were actually observed; and 2) when did house price forecasts that predicted house price declines first warn us that a decline in house prices was imminent?

There are two curious traits in the housing market data that suggest certain types of forecasting models may perform well. First, in the run-up to the recent house price declines, house prices were far above where they should have been given established long-run relationships among house prices, rental prices, and personal incomes. This suggests that error correction models grounded in economic theory, as suggested by Malpezzi (1999), Gallin (2006), and Gallin (2008) may hold the key to predicting turning points in the housing market. Second, a large, negative acceleration in house prices is observed in the last quarter of 2004. Random acceleration models and Hendry’s (2006) differenced vector equilibrium correction model may capture this deceleration and forecast successive quarters of decreasing growth, ultimately leading to declines in house prices.

My dissertation research directly addresses several of HUD’s stated strategic goals. Better house price forecasts will help reduce mortgage delinquencies, defaults, and foreclosures by ensuring that consumers know more about price risks when borrowing to purchase a home. Knowledge of turning points in the housing market can also be used by HUD and participants in the Home Loan Modification Program to reduce strategic defaults and improve program performance. Lenders, investors, and financial institutions would benefit from better house price forecasts by being able to better assess the default risk for individual loans and for their loan portfolios as a whole.

In general, homeownership is more sustainable and stable when both consumers and lenders enter into financial arrangements that are predictably beneficial and sustainable for both parties. Policymakers, able to detect bubbles and turning points in the housing market before they occur, would be able to implement counter-cyclical policies and prevent housing bubbles from growing larger, smooth market corrections, or prevent downturns all together. A healthier and less risky housing market with better informed policymakers would ultimately result in fewer foreclosures and less systemic risk, resulting in a more stable financial system.

To answer these research questions, I propose to estimate a number of different house price forecasting models using data up until the pre-crisis peak, and forecast over the following period of dramatic house price declines. I will evaluate and compare the resulting forecasts along a number of dimensions consistent with the forecasting literature, including mean-squared forecast error and bias. I will also perform parameter constancy tests and forecast encompassing tests in order to determine which models contain relevant and useful information. Preliminary results indicate that declines in house prices were forecastable before ever observing any actual house price declines. Multivariate error correction models based on economic theory and univariate models that incorporate house price acceleration changes offer the most promise of predicting and early-detecting turning points in the housing market.

Vox EU Article

NBER working paper released

http://www.nber.org/papers/w15455

Is Newer Better? Penn World Table Revisions and Their Impact on Growth Estimates

Simon Johnson, William Larson, Chris Papageorgiou, Arvind Subramanian

This paper sheds light on two problems in the Penn World Table (PWT) GDP estimates. First, we show that these estimates vary substantially across different versions of the PWT despite being derived from very similar underlying data and using almost identical methodologies; that this variability is systematic; and that it is intrinsic to the methodology deployed by the PWT to estimate growth rates. Moreover, this variability matters for the cross-country growth literature. While growth studies that use low frequency data remain robust to data revisions, studies that use annual data are less robust. Second, the PWT methodology leads to GDP estimates that are not valued at purchasing power parity (PPP) prices. This is surprising because the raison d’etre of the PWT is to adjust national estimates of GDP by valuing output at common international (purchasing power parity [PPP]) prices so that the resulting PPP-adjusted estimates of GDP are comparable across countries. We propose an approach to address these two problems of variability and valuation.